https://github.com/ialam085/local_store_sales_analysis_sql_server

This SQL report analyzes sales performance and manages inventory by flagging low-stock items for restocking, supporting data-driven decision-making.
https://github.com/ialam085/local_store_sales_analysis_sql_server

analysis excel sqlserver

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This SQL report analyzes sales performance and manages inventory by flagging low-stock items for restocking, supporting data-driven decision-making.

• Host: GitHub
• URL: https://github.com/ialam085/local_store_sales_analysis_sql_server
• Owner: ialam085
• Created: 2024-08-13T10:08:59.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-06T18:41:59.000Z (2 months ago)
• Last Synced: 2024-10-19T10:52:07.151Z (about 1 month ago)
• Topics: analysis, excel, sqlserver
• Homepage: https://github.com/ialam085/Local_Store_Sales_Analysis_SQL_SERVER
• Size: 85.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🔳 Local Store Sales Analysis ${\color{blue}(using\ SQL-SERVER)}$

${\color{red}Go\ to}$ 🔗 [SQL Queries](https://github.com/ialam085/Local_Store_Sales_Analysis_SQL_SERVER/blob/main/SQLQuery-Local_Store_Sales_Details.sql)

### ◻️ Objective

>The objective of the SQL report is to analyze various aspects of the store's sales data, including sales summaries, top-selling products, profit margins, and payment mode preferences. Additionally, it aims to manage inventory by identifying products with low quantities and suggesting restocking. This helps in making informed business decisions and ensuring efficient inventory management.

### ◻️ Tech Stack

>- SQL Server

>- Microsoft Excel

### ◻️ Steps includes

>- Data Refinement

>- Creating a Database and a Table

>- Importing Excel Dataset to SQL Server

>- Data Analysis by various SQL queries

### ◻️ Analysis includes

>- Sales Summary Analysis

>- Top-Selling Products Identification

>- Profit Margin Calculation

>- Profitability Categorization

>- Payment Mode Popularity Analysis

>- Inventory Level Monitoring

>- Restocking Suggestions

>- Aggregation of Sales Data

>- Product Category Performance Analysis

>- Transaction Count Analysis

### ◻️ Key Insights

>- **Total Sales by Category**: Electronics generated the highest sales with $12,393, highlighting strong demand in this category.

>- **Top-Selling Products**: The top 5 products collectively contributed $18,215 to total sales, emphasizing their popularity.

>- **Profit Margin Distribution**: The average profit margin across all products is 25%, with Electronics leading at 30%.

>- **High vs. Low Profit Products**: 40% of products fall into the 'High Profit' category, driving a significant portion of total profit.

>- **Popular Payment Modes**: Credit Card payments account for 50% of transactions, making it the most preferred payment method.

>- **Sales by Payment Mode**: EMI purchases contributed $8,725, indicating a preference for installment options among customers.

>- **Inventory Levels**: 20 products have quantities below 10, signaling a potential need for restocking to avoid stockouts.

>- **Restocking Alerts**: 15% of products are flagged for restocking, ensuring inventory levels remain optimal.

>- **Sales Contribution by Sub-Category**: The 'Phones' sub-category alone contributed $5,000 to total sales, underscoring its importance.

>- **Correlation Between Payment Mode and Category**: 60% of Electronics purchases were made via Credit Card, reflecting a strong correlation between this payment mode and the category.